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European Conference on Artificial Evolution

AE 1997: Artificial Evolution pp 53–68Cite as

Representations, fitness functions and genetic operators for the satisfiability problem

  • Genetic Operators
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1363))

Abstract

Two genetic algorithms for the satisfiability problem (SAT) are presented which mainly differ in the solution representation. We investigate these representations - the classical bit string representation and the path representation - with respect to their performance. We develop fitness functions which transform the traditional fitness landscape of SAT into more distinguishable ones. Furthermore, new genetic operators (mutation and crossover) are introduced. These genetic operators incorporate problem specific knowledge and thus, lead to increased performance in comparison to standard operators.

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References

  1. M. Davis and H. Putnam. A Computing Procedure for Quantification Theory. Journal of the ACM, Volume 7, 201–215, 1960

    Google Scholar 

  2. K. A. De Jong and W. M. Spears. Using Genetic Algorithms to Solve NPComplete Problems. In J. D. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms, 124–132, Morgan Kaufmann Publishers, San Mateo, CA, 1989

    Google Scholar 

  3. A. E. Eiben and J. K. van der Hauw. Graph Coloring with Adaptive Genetic Algorithms. Technical Report 96-11, Department of Computer Science, Leiden University, 1996

    Google Scholar 

  4. A. E. Eiben and J. K. van der Hauw. Solving 3-SAT with Adaptive Genetic Algorithms. In Proceedings of the 4th IEEE Conference on Evolutionary Computation, 81–86, IEEE Service Center, Piscataway, NJ, 1997

    Google Scholar 

  5. C. Fleurent and J. A. Ferland. Object-oriented Implementation of Heuristic Search Methods for Graph Coloring, Maximum Clique and Satisfiability. In D. S. Johnson and M. A. Trick (eds.), Cliques, Coloring and Satisfiability: 2nd DIMACS Implementation Challenge, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 26, 619–652, 1996

    Google Scholar 

  6. J. Frank. A Study of Genetic Algorithms to Find Approximate Solutions to Hard 3CNF Problems. Golden West International Conference on Artificial Intelligence, 1994

    Google Scholar 

  7. J. Frank. Weighting for Godot: Learning Heuristics for GSAT. In Proceedings of the 13th National Conference on Artificial Intelligence and the 8th Innovative Applications of Artificial Intelligence Conference, 338–343, 1996

    Google Scholar 

  8. J. Frank. Learning Short-Term Weights for GSAT. Submitted to 15th International Joint Conference on Artificial Intelligence, 1997

    Google Scholar 

  9. M. R. Carey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, San Francisco, CA, 1979

    Google Scholar 

  10. J. Cu. Global Optimization for Satisfiability (SAT) Problem. IEEE Transactions on Knowledge and Data Engineering, Volume 6, Number 3, 361–381, 1994

    Google Scholar 

  11. J.-K. Hao. A Clausal Genetic Representation and its Evolutionary Procedures for Satisfiability Problems. In D. W. Pearson, N. C. Steele, and R. F. Albrecht (eds.), Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms, 289–292, Springer, Wien, 1995

    Google Scholar 

  12. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Third Edition, Springer, 1996

    Google Scholar 

  13. D. Mitchell, B. Selman, and H. Levesque. Hard and Easy Distributions of SAT Problems. In Proceedings of the 10th National Conference on Artificial Intelligence, 459–465, 1992

    Google Scholar 

  14. K. Park. A Comparative Study of Genetic Search. In L. J. Eshelman (ed.), Proceedings of the Sixth International Conference on Genetic Algorithms, 512–519, Morgan Kaufmann, San Mateo, CA, 1995

    Google Scholar 

  15. B. Selman, H. A. Kautz, and B. Cohen. Noise Strategies for Improving Local Search. In Proceedings of the 12th National Conference on Artificial Intelligence, 337–343, 1994

    Google Scholar 

  16. B. Selman, H. Levesque, and D. Mitchell. A New Method for Solving Hard Satisfiability Problems. In Proceedings of the 10th National Conference on Artificial Intelligence, 440–446, 1992

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institut für Informatik, Technische Universität Clausthal, Erzstraße 1, D-38678, Clausthal-Zellerfeld, Germany

    Jens Gottlieb & Nico Voss

Authors
  1. Jens Gottlieb
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  2. Nico Voss
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Editor information

Jin-Kao Hao Evelyne Lutton Edmund Ronald Marc Schoenauer Dominique Snyers

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© 1998 Springer-Verlag Berlin Heidelberg

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Gottlieb, J., Voss, N. (1998). Representations, fitness functions and genetic operators for the satisfiability problem. In: Hao, JK., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds) Artificial Evolution. AE 1997. Lecture Notes in Computer Science, vol 1363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026590

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  • DOI: https://doi.org/10.1007/BFb0026590

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64169-8

  • Online ISBN: 978-3-540-69698-8

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